Deep Convolution Neural Networks (Deep CNNs) are the heart of the remarkable development in deep learning. CNNs have already been used in the 90's to solve the problem of character recognition, but their use has become as widespread as it is now thanks to recent research. These deep CNNs won the 2012 ImageNet image classification tournament, crushing other competitors. Then, the convolution neural network became a very useful tool in the field of the machine learning.
Meanwhile, image segmentation is a method of generating a label image by using an image, e.g., a training image or a test image as an input. As the deep learning has become popular recently, the deep learning is frequently used for the segmentation.
Recently, the post-processing is frequently used in the deep CNNs. The CNN plays several roles in an autonomous driving module. One of such roles is to detect one or more lanes in an input image. By detecting the lanes, a free space for vehicles to drive through may be detected, or vehicles may be appropriately controlled to drive on the center of a road.
However, if the result only from the deep CNNs is used, the performance of lane detection is not very useful. Hence, the lane detection is often achieved by post-processing the result from the deep CNNs. But, the lane detection by determining whether each pixel is included in the lanes does not have a good result when compared with the lane detection by setting at least one unit region included in the lanes.